Systems approach to improve disaster preparedness based on case studies

Preparedness planning for a disaster is an essential component in the process of the Disaster Risk Reduction (DRR), which is adhered by many disaster management professionals and authorities globally and locally. There, the question rises 'up to which extent of the disaster should the preparedness planning be done?'. To answer this question, knowledge on the extent of the disaster and its impact should be quantitatively established. When the impacts are considered, there will be impacts which have affected different components of given systems, due to the interrelations that exist within systems and its subcomponents. The spreading of impacts from one system to another due to these interrelations are called as the cascading effect. To identify the cascades and the components of systems, Emergency Operations Procedures (EOPs) and resilience frameworks are used in this study, and to quantify the cascade relationships as a proof of concept, a case study is used. The chosen case study area is Kaduwela Divisional Secretariat Division (DSD), for the 2016, 2017 and 2018 floods. Kaduwela DSD was severally hit by all three events, as Kelani river is running by the boundary of the DSD. For these floods, data on flood impact such as the number of affected people, establishment of relief camps, supply of food and dry rations and payment of compensation were gathered, as well as the data on flood hazard extent and the exposure such as flood extent maps, elevation maps of the Kaduwela area and the building footprint were gathered. Preliminary interpretations of the data revealed patterns and relationships that define the human behaviour after a flood, and more importantly, the rationales behind initiating the relief requirements were understood, along with the monetary requirements to satisfy those requirements. Furthermore, mathematical analyses were carried out to identify the regression relationships which predicted the human movement from the flood characteristics in a disaster. The tests were conducted on comparing the Pearson correlations and the multi criteria analysis, between the dependant and independent variables. The analyses revealed that there was more than one model to capture the effects to the humans having various inputs representing the flood characteristics. Therefore, all of the possible models were evaluated, by comparing the results from the models with the original data from the case study. By this, the best model to estimate the number of affected people and families was chosen and it was used as the quantifying relationship from flood characteristics to the human movement. The aforementioned mathematical relationships are then summarised in to the overall cascading effect diagram, which made the cascade diagram a cascade model. This cascade model is having the flood inundation area as the input parameter which is a flood characteristic. Therefore, now this quantified cascade model could be used to identify the number of people and houses affected, number of relief camps formed, expected costs of number of required facilities, number of security officials and healthcare officials required per relief camp, number of cooked food parcels, dry ration parcels and relief items required and the costs for those which are the output parameters of the cascade model. Furthermore, the model consists with the relationships to estimate the amounts for compensations for building structural and content damage in a disaster